Catastrophe Vulnerability and Risk Mapping in the Iron Quadrangle
Total Page：16
File Type：pdf, Size：1020Kb
Catastrophe Vulnerability and Risk Mapping in the Iron Quadrangle, Brazil – Preliminary Results in the Rio das Velhas Watershed Raphael de Vicq1, Teresa Albuquerque2, Rita Fonseca3, Mariangela Garcia Praça Leite4 1Earth Science Institute (ESI), University of Evora, Portugal and Federal University of Ouro Preto, Brazil 2Earth Science Institute (ESI), University of Evora and Polytechnique Institute of Castelo Branco| CERNAS|QRural, Av. Pedro Álvares Cabral, nº 12, 6000-084 Castelo Branco, Portugal, ORCID-0000-0002-8782-6133, [email protected] 3Earth Science Institute (ESI), Department of Geosciences, School of Science and Technology, AmbiTerra Laboratory, University of Évora, Portugal 4Department of Geology, School of Mines, Federal University of Ouro Preto, Brazil Abstract The geochemistry of fluvial deposits is one of the most used proxies for contamination assessment in mined areas. A river sediment survey was carried out in Iron Quadrangle (IQ), Brazil, between 2013 and 2015. Subsequently, in the last four years, two dam failures happened, causing great environmental damage. Geostatistical modelling was used to model Potentially Toxic Elements’ spatial patterns and the definition of hot/ cold clusters for Arsenic contamination risk concerning catastrophic scenarios, such as dam failure. and so used as a tool for vulnerability and risk assessment. The preliminary results for Arsenic spatial distribution are introduced and discussed. Keywords: Iron Quadrangle, Potentially Toxic Elements, Geostatistical Modelling Introduction (12.5% of total exports and 36.6% of the trade Fluvial sediments come from a myriad of balance – AMN 2019) and Minas Gerais sources, diffuse and punctual, whose relative State is responsible for the other 40% of the contribution varies over time and space. commercialized mineral production (AMN Their geochemical composition is a response 2019). The Iron Quadrangle (IQ), southeast to the availability of materials, whether due of Minas Gerais State, (fig. 1) is one of the to natural causes or human activities, such as most important mining producing provinces mining. Mining industries produce enormous in Brazil and one of the World’s most volumes of waste material, mainly tailings, important mineral regions, covering an area 2 which can be a relevant source of trace of approximately 7,000 km . Mining activities element contamination for the environment, are focus on iron and gold exploitations, especially in the river basins (Krıbek et al. whose wastes are stored in large tailings 2014). Worldwide, billions of tonnes of dams. The IQ yields the headwaters of two tailings are contained in impoundments major Brazilian River basins: Doce and São behind huge dams (Owen et al. 2020). Several Francisco. The last one, fed by Velhas (largest characteristics of these structures make them drainage basin in IQ) and Paraopeba rivers. more susceptible to failures.The enormous Unfortunately, during the last six years, volume and characteristics of the material IQ experience two of the most dramatic released in the environment with the collapse accidents involving the dam’s tailings failure. of the dam, especially in watersheds and river Fundão dam (Bento Rodrigues/Mariana), 7 3 basins, affect the quality of sediments and on 5 November 2015, released 4.3×10 m water (Kossoff et al. 2014). of tailings in Doce River basin (Carmo Mining is still among the most productive et al. 2017) and Córrego do Feijão dam activities that support the Brazilian economy (Brumadinho), on 25 January 2019, released Pope, J.; Wolkersdorfer, Ch.; Weber, A.; Sartz, L.; Wolkersdorfer, K. (Editors) 13 IMWA 2020 “Mine Water Solutions” 1.2×107 m3 of tailings in Paraopeba River river basins is mining activity (Deschamps basin (Thompson et al. 2020). & Matschullat, 2011). Several studies have A preliminary multivariate study was identified anomalous concentrations of performed through Principal Component arsenic in IQ waters and sediments (Costa Analysis (PCA) (Shaw 2003) to understand et al. 2015). In the case of sediments, there the attributes of preferential association and are reports of levels above 4700 µg.g-1 (Borba the reduction of the space of analyses. The et al. 2000), much higher than the average analysis begins with p random attributes concentration in the Earth’s crust (between X1, X2, …, Xp, where no assumption 1.0 – 4.8 µg.g-1, according to Taylor & of multivariate normality is required. McLennan, 1995 and Rudnick & Gao, 2003). Considering the ACP outputs, Cr, Zn, Cd, Arsenic is defined as a ‘Regionalized Ni, Cu and Pb were kept for further spatial Variable (Matheron 1971) and consequently analysis as they can act as accurate indicators additive by construction, since the mean for the pollution characterization. value within given observed support is equal In the herein survey the results for the to the arithmetic average of sample values, As spatial distribution are introduced and regardless of the statistical distribution of the discussed as As plays a key role in soil and values. Geostatistical modelling (Goovaerts sediments pollution (Gonzalez-Fernandez 1997, Journel and Huijbregts 1978) was et al. 2018). Indeed, among the elements used, throughout conventional variography present in iron and gold tailings, Arsenic followed by Sequential Gaussian Simulation appears as one of the most dangerous to algorithm (SGS) and local G clustering, the environment, including human health to model Arsenic concentration’s spatial (Fewtrell et al. 2005, Zhang et al., 2019). patterns and the definition of hot/cold Although the interaction between arsenic and spots for contamination risk. The Standard the various environmental compartments Deviation map, obtained from the performed (sediment, soil, water) still needs to be better one hundred simulations (SGS), allowed understood, there is no doubt that one of the the visualization of the correspondent main sources of arsenic contamination in spatial uncertainty and, therefore, acting Figure 1 Study area location. 14 Pope, J.; Wolkersdorfer, Ch.; Weber, A.; Sartz, L.; Wolkersdorfer, K. (Editors) IMWA 2020 “Mine Water Solutions” as a measurement of the obtained clusters with simple kriging (SK) using the normal robustness and providing a faster and score data and a zero mean (Goovaerts more intuitive way to verify whether the 1997). Once all normal scores had been problematic zones detected previously are simulated, they were back-transformed to true of concern, focusing on the visualization original grade values (Albuquerque et al., and delineation of potential zones for future 2017). For the computation, the Space- monitoring and remediation. Stat Software V. 4.0.18, Biomedwere, was used. The outcome of a simulation is a Methods twisted version of an estimation process, Five hundred and forty-one (541) stream which reproduces the statistics of the sediments were sampled throughout the known data, making a realistic look of the entire IQ (7,000 km2), providing a sampling exemplar, but providing a low prediction density of one sample per 13 km2. The behaviour. If multiple sequences of concentrations of the key PTEs: As; Cd; Co; simulation is designed, it is possible to Cr; Cu; Ni; Pb and Zn and associated metals obtain more reliable probabilistic maps; (Fe, Mn) were obtained through aqua regia 3. Finally, Local G clustering allowed digestion followed by ICP-AES (Spectro measurement of the degree of association Ciros CCD) analysis. that results from the concentration of A three-stepgeostatistical modelling weighted points (or region represented by methodology was used for the construction a weighted point) and all other weighted of the Arsenic spatial distribution maps and points included within a radius of distance the definition of spatial hot-spots, as follows: from the original (Getis and Ord 1992). 1. Selected attributes went through structural analysis and experimental When predicting the risk of contamination variograms were computed. The(e.g. months ahead), it is mandatory to variogram is a vector function used to stress the relevance of the chances for the compute the spatial variation structure of future estimated values exceeding maximum regionalized variables (Matheron 1971; admissible values. The delineation of zones of Journel and Huijbregts 1978); high and low risk requires the interpolation 2. Sequential Gaussian Simulation (SGS) of risk values to the nodes of a regular grid was used as a stochastic simulation making possible proper risk assessments, algorithm. SGS starts by defining and a prediction model working as guidance the univariate distribution of values, to a more sustainable environmental performing a normal score transform of management. The three watersheds (Doce, the original values to a standard normal Velhas, and Paraopeba) were processed distribution. Normal scores at grid node separately (fig. 2) guarantying that only the locations were simulated sequentially samples inside each geographic envelop were Doce Watershed Velhas Watershed Paraopeba Watershed Figure 2 Arsenic Spatial Distribution – SGS mean image. Pope, J.; Wolkersdorfer, Ch.; Weber, A.; Sartz, L.; Wolkersdorfer, K. (Editors) 15 IMWA 2020 “Mine Water Solutions” Figure 3 Arsenic Spatial Distribution – SGS mean image and spatial uncertainty (standard deviation). used for the correspondent spatial modelling. is possible to identify high rings (hot spots Finally, all the representations were projected for As pollution) and low rings (cold spots for together for visualization purposes (fig. 3 and As pollution)